Practical Deep RL Application with DQN and CNN

This page was last edited on 29 April 2025

Why this tutorial?

Because most Deep Reinforcement Learning(RL) tutorials are too abstract. And if they’re not abstract, they’re often split into separate parts. Disconnected. Hard to follow.

Or worse — so complex that you lose track after a few lines of code. 

This one is real, visual, and works in Google Colab.

You’ll train an agent to answer one simple question: “Is the digit in this image a 3?“

Binary decision. Simple task. Real RL logic.

The tutorial is broken into logical, modular steps. Each step builds on the one before it. No missing links. No skipping the hard parts.

Perfect if you want to learn by doing.

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What you will learn

→ How to define a Reinforcement Learning(RL) problem from scratch.

→ How to translate that into a Markov Decision Process(MDP).

→ How to choose the right algorithm.

→ How to build your environment and Convolutional Neural Network(CNN) model.

→ How to train, test, and improve your agent.

→ How to run it live in Google Colab.

You’ll understand every line of code. And you’ll see how to apply this logic to real applications later.

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Structure of the tutorial

This tutorial has six parts:

→ PART 1: Overview of the tutorial ← you are here

→ PART 2: Problem Definition

→ PART 3: Markov Decision Process (MDP)

→ PART 4: Choosing the Algorithm (DQN)

→ PART 5: Environment + RL Model + Reward Function

→ PART 6: Training + Testing + Google Colab Access

Each page is short, clear, and focused. You can follow everything step by step.

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Core Methods and Concepts in This Tutorial

This is not just an example with some code. You’ll use real tools and methods from Deep Reinforcement Learning.

Some of the key ideas we’ll cover:

• Mathematical foundations: vectors, derivatives, gradients, spaces, normalization, function approximation
• RL concepts: agent, reward shaping, Bellman equation, on/off-policy learning
• Learning strategies: choosing algorithms, epsilon-greedy, experience replay, SIM2REAL
• Deep RL techniques: backpropagation, artificial neuron, ReLU, weight updates, gradient descent

Each concept is explained simply, with clear steps and code examples.

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Why Deep Reinforcement Learning?

Because RL is about decision-making. Not just prediction. Not just classification. It’s about learning how to act.

Deep RL helps an agent learn from trial and error. Without labeled data and without human supervision.

For example, it’s how robots learn to move. It’s how autonomous cars learn to avoid obstacles. And it’s how your agent will learn to detect the digit 3.

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Where can this logic be applied?

The structure you learn here can be reused in:

→ Robotics: binary classification + control (e.g., is this a tree or not?).

→ Agriculture: detect fruits, weeds, water zones.

→ Manufacturing: defect vs non-defect.

→ Healthcare: classify risk, detect anomalies.

→ Smart homes: trigger actions from visual input.

Anywhere a system needs to observe → decide → act.

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My recommendation

Don’t jump directly to the code. First, read the full tutorial, page by page. It will save you hours later.

After that, you can run everything in Google Colab in 2 minutes.

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Next: Problem Definition

Let’s define the problem clearly.

What is the task?

What is success?

What feedback will the agent receive?

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Part 2: Problem Definition